Fine Grained Image Classification On Sun397
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Accuracy | Paper Title | Repository |
|---|---|---|---|
| Bamboo (ViT-B/16) | 79.5 | Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy | |
| SEER (RegNet10B - linear eval) | 80.0 | Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision | |
| TWIST (ResNet-50) | 67.4 | Self-Supervised Learning by Estimating Twin Class Distributions | - |
| µ2Net (ViT-L/16) | 84.8 | An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems | |
| NNCLR | 62.5 | With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations |
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